Backpropagation, discovered by Paul Werbos in the 1970s, is the fundamental algorithm that trains virtually all modern AI models including large language models like LLaMA. The algorithm uses calculus and the chain rule to efficiently compute gradients - the slopes of the loss function with respect to each model parameter. These gradients guide the learning process by indicating how to adjust parameters to reduce prediction errors. The explanation demonstrates backpropagation through a simplified GPS coordinate classification model, showing how the algorithm scales from basic linear models to complex neural networks capable of learning intricate patterns in high-dimensional spaces.
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